Automatic Text Simplification for Social Good: Progress and Challenges

Abstract

Since the late 1990s, automatic text simplification (ATS) was promoted as a natural language processing (NLP) task with great potential to make texts more accessible to people with various reading or cognitive disabilities, and enable their better social inclusion. Large multidisciplinary projects showed promising steps in that direction. Since 2010, the field started attracting more attention but at the cost of major shifts in system architecture, target audience, and evaluation strategies. Somewhere along the way, the focus has shifted from ATS for social good towards building complex end-to-end neural architectures that are not aimed at any particular target population. This study presents the trajectory of ATS for social good, the main issues in current ATS trends, and the ways forward that could bring the field back to its initial goals.